Traditional　collaborative　filtering　techniques　suffer　from　the　data　sparsity　problem　in　practice.　That　is,　only　a　small　proportion　of　all　items　in　the　recommender　system　occur　in　a　user＇s　rated　item　list.　However,　in　order　to　retrieve　items　meeting　a　user＇s　interest,　all　possible　candidate　items　should　be　investigated.　To　address　this　problem,　this　paper　proposes　a　recommendation　approach　called　DeepRec,　based　on　feedforward　deep　neural　network　learning　with　item　embedding　and　weighted　loss　function.　Specifically,　item　embedding　learns　numerical　vectors　for　item　representation,　and　weighted　loss　function　balances　popularity　and　novelty　of　recommended　items.　Moreover,　it　introduces　two　strategies,　i.e.　sampling　by　random　(Ran-Strategy)　and　sampling　by　distribution　(Pro-Strategy),　to　leave　one　item　as　output　and　the　remaining　as　input　from　each　user＇s　historically　rated　item　list.　Max-pooling　and　average-pooling　are　employed　to　combine　individual　item　vectors　to　derive　users＇　input　vectors　for　feedforward　deep　neural　network　learning.　Experiments　on　the　App　dataset　and　the　Last.fm　dataset　demonstrate　that　the　proposed　DeepRec　approach　is　superior　to　state-of-the-art　techniques　in　recommending　Apps　and　songs　in　terms　of　accuracy　and　diversity　as　well　as　complexity.　(C)　2018　Elsevier　Inc.　All　rights　reserved.